{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook regroups the code sample of the video below, which is a part of the [Hugging Face course](https://huggingface.co/course)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/1m7BerpSq8A?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/1m7BerpSq8A?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the Transformers and Datasets libraries to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install datasets transformers[sentencepiece]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset, load_metric\n",
    "\n",
    "raw_datasets = load_dataset(\"xsum\")\n",
    "raw_datasets = raw_datasets.remove_columns([\"id\"])\n",
    "raw_datasets[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(raw_datasets[\"train\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "model_checkpoint = \"t5-small\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
    "\n",
    "sample = raw_datasets[\"train\"][1]\n",
    "inputs = tokenizer(sample[\"document\"])\n",
    "with tokenizer.as_target_tokenizer():\n",
    "    targets = tokenizer(sample[\"summary\"])\n",
    "\n",
    "print(tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"]))\n",
    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_input_length = 1024\n",
    "max_target_length = 128\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    model_inputs = tokenizer(examples[\"document\"], max_length=max_input_length, truncation=True)\n",
    "\n",
    "    # Setup the tokenizer for targets\n",
    "    with tokenizer.as_target_tokenizer():\n",
    "        labels = tokenizer(examples[\"summary\"], max_length=max_target_length, truncation=True)\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs\n",
    "\n",
    "tokenized_datasets = raw_datasets.map(\n",
    "    preprocess_function, batched=True, remove_columns=[\"document\", \"summary\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForSeq2Seq\n",
    "\n",
    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Data processing for Summarization",
   "provenance": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
